Soham Kulkarni

Had a great time attending the Product Thinking and the Future of EPD(Engineering, product and Design) as an Engineering Leader event hosted by ELC today! A big thank you to ELC and Commure for organizing this thoughtful and engaging session—and to the amazing panel, including Dhruv Parthasarathy, Nissim Lehyani, James Tyack,Patrick Gallagher, for sharing their candid insights on how the traditional boundaries between Engineering, Product, and Design are rapidly evolving in the age of AI. Here are a few takeaways that really stuck with me: 🔹 Companies are shifting away from static PRDs and increasingly using prototypes as a discovery tool—validating ideas with customers early and often. 🔹 Hiring is changing: it’s less about checking boxes and more about finding problem solvers and clear thinkers who can adapt and lead. 🔹 When it comes to LLMs, everyone has their own “AHA moment.” Until then, keep exploring. You’ve essentially got the smartest person in your pocket—start using it to solve real problems. Excited to bring these insights back to my team and continue experimenting at the intersection of product, engineering, and AI! #EngineeringLeadership #AI #ProductThinking #ELC #LLM #TechLeadership #Prototyping #EPD

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There is a quiet habit in many organizations that I believe we should rethink. Calling engineers "resources". At first glance, it may sound harmless. But words shape culture, and the difference between an engineer and a resource is deeper than terminology. A resource is something you allocate. A resource is interchangeable. A resource is planned in spreadsheets and capacity charts. But an engineer is something entirely different. An engineer is a problem solver. An engineer designs systems that didn’t exist before. An engineer takes ambiguity, constraints, and trade-offs and turns them into working reality. You cannot allocate creativity. You cannot schedule curiosity. You cannot spreadsheet innovation. When we call people "resources", we unintentionally reduce them to units of output. When we call them engineers, we acknowledge their judgment, craft, and ownership. The difference shows up in how teams behave. When people are treated like resources: • Work is assigned. • Thinking is minimised. • Ownership is diluted. When people are treated like engineers: • Problems are shared. • Ideas are encouraged. • Systems improve. Great companies are not built by managing resources efficiently. They are built by empowering engineers to think, question, and create. Language matters because culture follows language. Maybe the shift is simple: Instead of asking, "Do we have enough resources?" Ask, "Do we have the right engineers solving the right problems?" Small change in words. Massive change in mindset. #eBay #EngineeringCulture #Leadership #TechLeadership #SoftwareEngineering

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At AI World Vegas, Oracle’s Sharat Chander told The Times of India that young engineers can stay relevant by strengthening their fundamentals — from concurrency and performance tuning to API design — and using AI tools to accelerate, not replace, learning. https://lnkd.in/gnjfieu9

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From Mid-Scale Hustle to Big-Scale Systems — My Leadership Journey When I worked at a mid-sized company — McAfee (formerly Intel Security), life as an engineering leader looked very different. I owned the entire tech stack end-to-end for the Real Protect SDK, which powered both enterprise and consumer security products. Decisions were quick. If I had an idea, we could experiment and roll it out within days. The visibility was immediate — the team saw the impact directly, and customers felt it instantly. But there were trade-offs: scale was limited, resources were stretched, and reliability sometimes gave way to speed. I wore multiple hats — tech lead, architect, and often the “on-call fireman.” Fast forward to today at Amazon. The canvas is massive — whether rolling out Prime membership features or building a multi-tenant portal to enable supply chain services for 1P & 3P sellers. Systems serve millions of users globally, reliability is non-negotiable, and playbooks ensure consistency across teams. I learn from some of the brightest minds, and every design decision forces me to think about scale, latency, and resilience. But here too, there are trade-offs: processes are heavier, decisions take longer, and ownership is often of just one slice of a much bigger puzzle. ⚡ What helped me move faster in this environment: Clarity of priorities → tie every discussion back to business + customer outcomes Cross-team relationships → build trust before you need alignment Document → Decide → Drive → cut endless meetings with written context & trade-offs Bias for small wins → ship incremental value instead of waiting for “big bang” launches Empowering the team → unblock quickly and let them own execution 💡 My takeaway: Mid-scale sharpened my ownership, versatility, and bias for action. Big-scale honed my systems thinking, discipline, and focus on reliability. The best leaders blend both — carrying startup agility into enterprise scale. 👉 If you’ve made the shift between mid and big companies, what was your biggest “surprise learning”? #EngineeringLeadership #TechLeadership #CareerGrowth #EngineeringManagement #ScalingTech #BigTech #StartUpCulture #LeadershipJourney

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With the continued evolution of LLMs and AI apps, some people may find it overwhelming to pick the one(s) relevant to their specific use cases. I have consolidated some of the popular ones with a brief description and their application areas for quick reference (and launch directly from the web page below). They are also categorized into separate lists based on use cases e.g., Text generation & summarization, Image generation, Code, Audio, Video, etc. Hope this would be useful.   Feel free to check it out and put in comments below for any latest ones that may be missing on the list. Will update later this week. #GenAI #AI #ML #LLM #AgenticAI #Cloud https://lnkd.in/gQhcu8Ud

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Enjoyed collaborating with Josephine Joyce on this DZone article: https://lnkd.in/gAhNzXd9 . In Performance-Focused Platform Engineering, we explore why platform teams must evolve beyond enablement and start engineering for measurable performance outcomes. Curious to hear how others are thinking about this. #PlatformEngineering #DevOps #SRE #CloudNative #IDP

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